Zobrazeno 1 - 10
of 529
pro vyhledávání: '"Li Guofa"'
Autor:
Liao, Haicheng, Li, Yongkang, Li, Zhenning, Wang, Chengyue, Tian, Chunlin, Huang, Yuming, Bian, Zilin, Zhu, Kaiqun, Li, Guofa, Pu, Ziyuan, Hu, Jia, Cui, Zhiyong, Xu, Chengzhong
Accurately and safely predicting the trajectories of surrounding vehicles is essential for fully realizing autonomous driving (AD). This paper presents the Human-Like Trajectory Prediction model (HLTP++), which emulates human cognitive processes to i
Externí odkaz:
http://arxiv.org/abs/2407.07020
Autor:
Kou, Wei-Bin, Lin, Qingfeng, Tang, Ming, Xu, Sheng, Ye, Rongguang, Leng, Yang, Wang, Shuai, Li, Guofa, Chen, Zhenyu, Zhu, Guangxu, Wu, Yik-Chung
Deep learning-based Autonomous Driving (AD) models often exhibit poor generalization due to data heterogeneity in an ever domain-shifting environment. While Federated Learning (FL) could improve the generalization of an AD model (known as FedAD syste
Externí odkaz:
http://arxiv.org/abs/2405.04146
Autor:
Liao, Haicheng, Li, Zhenning, Wang, Chengyue, Shen, Huanming, Wang, Bonan, Liao, Dongping, Li, Guofa, Xu, Chengzhong
This paper introduces a trajectory prediction model tailored for autonomous driving, focusing on capturing complex interactions in dynamic traffic scenarios without reliance on high-definition maps. The model, termed MFTraj, harnesses historical traj
Externí odkaz:
http://arxiv.org/abs/2405.01266
Autor:
Liao, Haicheng, Li, Zhenning, Wang, Chengyue, Wang, Bonan, Kong, Hanlin, Guan, Yanchen, Li, Guofa, Cui, Zhiyong, Xu, Chengzhong
As autonomous driving technology progresses, the need for precise trajectory prediction models becomes paramount. This paper introduces an innovative model that infuses cognitive insights into trajectory prediction, focusing on perceived safety and d
Externí odkaz:
http://arxiv.org/abs/2404.17520
Autor:
Liao, Haicheng, Li, Zhenning, Shen, Huanming, Zeng, Wenxuan, Liao, Dongping, Li, Guofa, Li, Shengbo Eben, Xu, Chengzhong
The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles. To address this challenge, we pioneer a novel behavior-aware trajectory prediction model (BAT) that
Externí odkaz:
http://arxiv.org/abs/2312.06371
Autor:
Liao, Haicheng, Shen, Huanming, Li, Zhenning, Wang, Chengyue, Li, Guofa, Bie, Yiming, Xu, Chengzhong
In the field of autonomous vehicles (AVs), accurately discerning commander intent and executing linguistic commands within a visual context presents a significant challenge. This paper introduces a sophisticated encoder-decoder framework, developed t
Externí odkaz:
http://arxiv.org/abs/2312.03543
Publikováno v:
E3S Web of Conferences, Vol 233, p 04035 (2021)
In this paper, combined with the actual engineering, a modular transportation status monitoring system is designed, which can be used to measure environmental conditions such as vibration, inclination, temperature and humidity during the process of t
Externí odkaz:
https://doaj.org/article/32cae20e810341928ef5b0a7a081afac
Publikováno v:
MATEC Web of Conferences, Vol 336, p 02027 (2021)
Combined with the actual project, a grating ruler accelerated life test device is designed, which can simulate the actual loads, including temperature, humidity and speed stress. An accelerated life test scheme based on stepped stress loading is prop
Externí odkaz:
https://doaj.org/article/7c2d54e1eeb84a4da987f5a7bffc5f44
Autor:
Liao, Haicheng, Shen, Huanming, Li, Zhenning, Wang, Chengyue, Li, Guofa, Bie, Yiming, Xu, Chengzhong
Publikováno v:
In Communications in Transportation Research December 2024 4
Publikováno v:
In Structures December 2024 70